Predicting sales accurately would make a lot of difference for a lot of companies, Walmart is definitely one of them. Although labor cost is the biggest expense for Walmart, inventory cost follows it by just a margin. Walmart spent a whopping $56 billion on inventory in 2022. Walmart did not expose the amount of waste last year, but it did face a lot of backlash for wasting food in 2022. So in this project, I worked on Walmart dataset to predict accurate sales for Walmart with the hopes of accurate prediction and less waste. The goal of the project is to build a model to predict sales for Walmart stores.
This dataset from Kaggle contains data on Walmart sales, which can be found here. The dataset includes information about sales, stores, departments, and holidays. Some of the features included are store number, temperature, fuel price, and markdown data.
The primary objective of this project is to leverage the Walmart Sales Forecast dataset to develop robust predictive models that can accurately forecast future sales. By harnessing machine learning techniques, we aim to uncover patterns and trends within the data, enabling better decision-making for store managers, supply chain professionals, and business strategists. The insights gained from this project can empower Walmart and similar retailers to optimize inventory, streamline operations, and enhance overall business performance.
I used Linear Regression to predict the data and managed to find the following results:
Mean Absolute Error: 1597.28
Mean Squared Error: 15834541.84
R-Square: 0.97
This dataset is a valuable resource for anyone interested in building a model to predict sales. The dataset includes a variety of features that can be used to build an accurate model. This model can be used to help Walmart with tasks such as arranging stock, calculating revenue, and making investment decisions. By harnessing the power of data analytics and machine learning, this project aims to enhance Walmart's ability to forecast sales accurately, ultimately leading to improved business performance.